Hourly Data without Outliers

Dataset statistics

Number of variables	19
Number of observations	346
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	36.2 KiB
Average record size in memory	107.3 B

Variable types

Numeric	10
Categorical	6
Boolean	3

Alerts

`rental_date` has a high cardinality: 167 distinct values	High cardinality
`df_index` is highly correlated with `rental_month` and 1 other fields	High correlation
`rental_month` is highly correlated with `df_index` and 1 other fields	High correlation
`temp` is highly correlated with `df_index` and 1 other fields	High correlation
`df_index` is highly correlated with `rental_month`	High correlation
`rental_month` is highly correlated with `df_index`	High correlation
`df_index` is highly correlated with `rental_month` and 1 other fields	High correlation
`rental_hour` is highly correlated with `rental_year`	High correlation
`rental_day` is highly correlated with `rental_year`	High correlation
`rental_month` is highly correlated with `df_index` and 1 other fields	High correlation
`rental_year` is highly correlated with `df_index` and 3 other fields	High correlation
`dayofweek` is highly correlated with `working_day` and 1 other fields	High correlation
`working_day` is highly correlated with `dayofweek` and 1 other fields	High correlation
`peak` is highly correlated with `dayofweek` and 1 other fields	High correlation
`df_index` is highly correlated with `rental_month` and 3 other fields	High correlation
`rental_hour` is highly correlated with `peak` and 1 other fields	High correlation
`rental_day` is highly correlated with `temp` and 1 other fields	High correlation
`rental_month` is highly correlated with `df_index` and 5 other fields	High correlation
`rental_year` is highly correlated with `df_index` and 1 other fields	High correlation
`dayofweek_n` is highly correlated with `dayofweek` and 1 other fields	High correlation
`dayofweek` is highly correlated with `dayofweek_n` and 1 other fields	High correlation
`working_day` is highly correlated with `rental_month` and 4 other fields	High correlation
`season` is highly correlated with `df_index` and 3 other fields	High correlation
`peak` is highly correlated with `rental_hour` and 1 other fields	High correlation
`timesofday` is highly correlated with `rental_hour`	High correlation
`temp` is highly correlated with `df_index` and 4 other fields	High correlation
`count_day` is highly correlated with `rental_day` and 2 other fields	High correlation
`df_index` has unique values	Unique
`dayofweek_n` has 32 (9.2%) zeros	Zeros

Reproduction

Analysis started	2022-04-12 18:40:09.759801
Analysis finished	2022-04-12 18:40:21.990051
Duration	12.23 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	346
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2039.089595

Minimum	12
Maximum	6675
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	12
5-th percentile	134.25
Q1	671.25
median	1810
Q3	3226.5
95-th percentile	4790.25
Maximum	6675
Range	6663
Interquartile range (IQR)	2555.25

Descriptive statistics

Standard deviation	1539.584256
Coefficient of variation (CV)	0.7550351194
Kurtosis	-0.603021373
Mean	2039.089595
Median Absolute Deviation (MAD)	1207.5
Skewness	0.5892219283
Sum	705525
Variance	2370319.682
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
12	1	0.3%
2568	1	0.3%
2664	1	0.3%
2654	1	0.3%
2635	1	0.3%
2633	1	0.3%
2630	1	0.3%
2611	1	0.3%
2610	1	0.3%
2587	1	0.3%
Other values (336)	336	97.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
12	1	0.3%
29	1	0.3%
42	1	0.3%
59	1	0.3%
84	1	0.3%
93	1	0.3%
95	1	0.3%
96	1	0.3%
97	1	0.3%
99	1	0.3%

Value	Count	Frequency (%)
6675	1	0.3%
6494	1	0.3%
6473	1	0.3%
6195	1	0.3%
5325	1	0.3%
5104	1	0.3%
4996	1	0.3%
4962	1	0.3%
4961	1	0.3%
4924	1	0.3%

rental_date
Categorical

HIGH CARDINALITY

Distinct	167
Distinct (%)	48.3%
Missing	0
Missing (%)	0.0%
Memory size	2.8 KiB

2021-04-24	7
2021-04-25	7
2021-03-07	7
2021-03-17	7
2021-04-03	7
Other values (162)	311

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	83 ?
Unique (%)	24.0%

Sample

1st row	2021-03-01
2nd row	2021-03-02
3rd row	2021-03-03
4th row	2021-03-04
5th row	2021-03-05

Common Values

Value	Count	Frequency (%)
2021-04-24	7	2.0%
2021-04-25	7	2.0%
2021-03-07	7	2.0%
2021-03-17	7	2.0%
2021-04-03	7	2.0%
2021-04-04	6	1.7%
2021-04-17	6	1.7%
2021-06-29	5	1.4%
2021-03-27	5	1.4%
2021-04-10	5	1.4%
Other values (157)	284	82.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2021-04-24	7	2.0%
2021-03-07	7	2.0%
2021-03-17	7	2.0%
2021-04-03	7	2.0%
2021-04-25	7	2.0%
2021-04-04	6	1.7%
2021-04-17	6	1.7%
2021-04-02	5	1.4%
2021-07-23	5	1.4%
2021-03-06	5	1.4%
Other values (157)	284	82.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

rental_hour
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	17
Distinct (%)	4.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.58959538

Minimum	0
Maximum	23
Zeros	1
Zeros (%)	0.3%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	9
Q1	11
median	13
Q3	16
95-th percentile	19
Maximum	23
Range	23
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.146529049
Coefficient of variation (CV)	0.2315395684
Kurtosis	0.168789465
Mean	13.58959538
Median Absolute Deviation (MAD)	2
Skewness	0.1107479614
Sum	4702
Variance	9.900645053
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=17)

Value	Count	Frequency (%)
12	52	15.0%
11	46	13.3%
13	41	11.8%
18	30	8.7%
15	30	8.7%
10	27	7.8%
17	27	7.8%
14	26	7.5%
16	24	6.9%
9	13	3.8%
Other values (7)	30	8.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.3%
8	10	2.9%
9	13	3.8%
10	27	7.8%
11	46	13.3%
12	52	15.0%
13	41	11.8%
14	26	7.5%
15	30	8.7%
16	24	6.9%

Value	Count	Frequency (%)
23	1	0.3%
22	1	0.3%
21	1	0.3%
20	5	1.4%
19	11	3.2%
18	30	8.7%
17	27	7.8%
16	24	6.9%
15	30	8.7%
14	26	7.5%

rental_day
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	30
Distinct (%)	8.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.10115607

Minimum	1
Maximum	30
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	8
median	16
Q3	22
95-th percentile	29
Maximum	30
Range	29
Interquartile range (IQR)	14

Descriptive statistics

Standard deviation	8.525062716
Coefficient of variation (CV)	0.5645304688
Kurtosis	-1.146158321
Mean	15.10115607
Median Absolute Deviation (MAD)	7
Skewness	-0.002382658239
Sum	5225
Variance	72.67669431
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=30)

Value	Count	Frequency (%)
17	20	5.8%
16	20	5.8%
3	18	5.2%
8	15	4.3%
2	15	4.3%
13	14	4.0%
4	14	4.0%
19	13	3.8%
11	12	3.5%
22	12	3.5%
Other values (20)	193	55.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	10	2.9%
2	15	4.3%
3	18	5.2%
4	14	4.0%
5	6	1.7%
6	9	2.6%
7	11	3.2%
8	15	4.3%
9	5	1.4%
10	12	3.5%

Value	Count	Frequency (%)
30	8	2.3%
29	11	3.2%
28	10	2.9%
27	11	3.2%
26	9	2.6%
25	11	3.2%
24	12	3.5%
23	11	3.2%
22	12	3.5%
21	9	2.6%

rental_month
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	3.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.953757225

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	4
median	6
Q3	8
95-th percentile	10
Maximum	12
Range	11
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.519063615
Coefficient of variation (CV)	0.4231048595
Kurtosis	-0.9594381869
Mean	5.953757225
Median Absolute Deviation (MAD)	2
Skewness	0.4300097308
Sum	2060
Variance	6.345681495
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
3	67	19.4%
4	66	19.1%
7	43	12.4%
6	41	11.8%
5	29	8.4%
10	29	8.4%
8	28	8.1%
9	24	6.9%
11	14	4.0%
2	3	0.9%
Other values (2)	2	0.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1	0.3%
2	3	0.9%
3	67	19.4%
4	66	19.1%
5	29	8.4%
6	41	11.8%
7	43	12.4%
8	28	8.1%
9	24	6.9%
10	29	8.4%

Value	Count	Frequency (%)
12	1	0.3%
11	14	4.0%
10	29	8.4%
9	24	6.9%
8	28	8.1%
7	43	12.4%
6	41	11.8%
5	29	8.4%
4	66	19.1%
3	67	19.4%

rental_year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	2.8 KiB

2021	342
2022	4

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2021
2nd row	2021
3rd row	2021
4th row	2021
5th row	2021

Common Values

Value	Count	Frequency (%)
2021	342	98.8%
2022	4	1.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2021	342	98.8%
2022	4	1.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

holiday
Boolean

Distinct	2
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	598.0 B

False	317
True	29

Common Values
Chart

Value	Count	Frequency (%)
False	317	91.6%
True	29	8.4%

dayofweek_n
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	2.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.502890173

Minimum	0
Maximum	6
Zeros	32
Zeros (%)	9.2%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	4
Q3	5
95-th percentile	6
Maximum	6
Range	6
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	1.938546527
Coefficient of variation (CV)	0.5534134475
Kurtosis	-1.099432829
Mean	3.502890173
Median Absolute Deviation (MAD)	1
Skewness	-0.3969883327
Sum	1212
Variance	3.757962637
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
5	84	24.3%
6	56	16.2%
4	54	15.6%
1	41	11.8%
3	41	11.8%
2	38	11.0%
0	32	9.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	32	9.2%
1	41	11.8%
2	38	11.0%
3	41	11.8%
4	54	15.6%
5	84	24.3%
6	56	16.2%

Value	Count	Frequency (%)
6	56	16.2%
5	84	24.3%
4	54	15.6%
3	41	11.8%
2	38	11.0%
1	41	11.8%
0	32	9.2%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	7
Distinct (%)	2.0%
Missing	0
Missing (%)	0.0%
Memory size	830.0 B

Saturday	84
Sunday	56
Friday	54
Thursday	41
Tuesday	41
Other values (2)	70

Length

Max length	9
Median length	7
Mean length	7.170520231
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Monday
2nd row	Tuesday
3rd row	Wednesday
4th row	Thursday
5th row	Friday

Common Values

Value	Count	Frequency (%)
Saturday	84	24.3%
Sunday	56	16.2%
Friday	54	15.6%
Thursday	41	11.8%
Tuesday	41	11.8%
Wednesday	38	11.0%
Monday	32	9.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
saturday	84	24.3%
sunday	56	16.2%
friday	54	15.6%
thursday	41	11.8%
tuesday	41	11.8%
wednesday	38	11.0%
monday	32	9.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	598.0 B

True	188
False	158

Common Values
Chart

Value	Count	Frequency (%)
True	188	54.3%
False	158	45.7%

season
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	678.0 B

Spring	138
Summer	101
Winter	54
Autumn	53

Length

Max length	6
Median length	6
Mean length	6
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Winter
2nd row	Winter
3rd row	Winter
4th row	Winter
5th row	Winter

Common Values

Value	Count	Frequency (%)
Spring	138	39.9%
Summer	101	29.2%
Winter	54	15.6%
Autumn	53	15.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
spring	138	39.9%
summer	101	29.2%
winter	54	15.6%
autumn	53	15.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	598.0 B

False	250
True	96

Common Values
Chart

Value	Count	Frequency (%)
False	250	72.3%
True	96	27.7%

timesofday
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	678.0 B

Afternoon	200
Morning	96
Evening	48
Night	2

Length

Max length	9
Median length	9
Mean length	8.144508671
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Evening
2nd row	Evening
3rd row	Afternoon
4th row	Morning
5th row	Afternoon

Common Values

Value	Count	Frequency (%)
Afternoon	200	57.8%
Morning	96	27.7%
Evening	48	13.9%
Night	2	0.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
afternoon	200	57.8%
morning	96	27.7%
evening	48	13.9%
night	2	0.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

temp
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	163
Distinct (%)	47.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.48815029

Minimum	3.3
Maximum	26.3
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	3.3
5-th percentile	5.575
Q1	10.1
median	13.2
Q3	17.2
95-th percentile	22.175
Maximum	26.3
Range	23
Interquartile range (IQR)	7.1

Descriptive statistics

Standard deviation	4.902079244
Coefficient of variation (CV)	0.3634359893
Kurtosis	-0.644429132
Mean	13.48815029
Median Absolute Deviation (MAD)	3.7
Skewness	0.160060788
Sum	4666.9
Variance	24.03038092
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10.8	7	2.0%
10.1	7	2.0%
11.4	6	1.7%
11.3	6	1.7%
8.8	6	1.7%
16.3	5	1.4%
13.6	5	1.4%
10.3	5	1.4%
17.7	5	1.4%
16.9	5	1.4%
Other values (153)	289	83.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
3.3	2	0.6%
3.5	1	0.3%
4.1	1	0.3%
4.2	1	0.3%
4.5	1	0.3%
4.6	2	0.6%
4.7	1	0.3%
4.8	2	0.6%
5.1	3	0.9%
5.2	1	0.3%

Value	Count	Frequency (%)
26.3	2	0.6%
24.3	1	0.3%
23.7	1	0.3%
23.5	1	0.3%
23.4	2	0.6%
23.3	1	0.3%
23.2	1	0.3%
22.9	1	0.3%
22.7	1	0.3%
22.6	1	0.3%

rhum
Real number (ℝ_≥0)

Distinct	58
Distinct (%)	16.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	68.36705202

Minimum	24
Maximum	98
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	24
5-th percentile	47.25
Q1	60
median	69
Q3	76
95-th percentile	88.75
Maximum	98
Range	74
Interquartile range (IQR)	16

Descriptive statistics

Standard deviation	12.48377317
Coefficient of variation (CV)	0.1825992608
Kurtosis	0.06790344061
Mean	68.36705202
Median Absolute Deviation (MAD)	8
Skewness	-0.1725588781
Sum	23655
Variance	155.8445924
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
69	14	4.0%
67	13	3.8%
72	13	3.8%
73	13	3.8%
71	13	3.8%
79	13	3.8%
63	13	3.8%
74	12	3.5%
64	12	3.5%
68	11	3.2%
Other values (48)	219	63.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
24	1	0.3%
36	1	0.3%
38	1	0.3%
39	1	0.3%
40	2	0.6%
41	2	0.6%
43	2	0.6%
44	5	1.4%
45	2	0.6%
47	1	0.3%

Value	Count	Frequency (%)
98	2	0.6%
97	1	0.3%
96	2	0.6%
94	3	0.9%
91	3	0.9%
90	1	0.3%
89	6	1.7%
88	4	1.2%
87	6	1.7%
86	5	1.4%

wdsp
Real number (ℝ_≥0)

Distinct	20
Distinct (%)	5.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.043352601

Minimum	2
Maximum	22
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	2
5-th percentile	4
Q1	6
median	9
Q3	11
95-th percentile	15.75
Maximum	22
Range	20
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.56811553
Coefficient of variation (CV)	0.3945567189
Kurtosis	0.6473410997
Mean	9.043352601
Median Absolute Deviation (MAD)	3
Skewness	0.7274960821
Sum	3129
Variance	12.73144844
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
6	49	14.2%
7	37	10.7%
9	36	10.4%
8	34	9.8%
10	34	9.8%
12	28	8.1%
11	26	7.5%
5	24	6.9%
4	16	4.6%
13	16	4.6%
Other values (10)	46	13.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
2	3	0.9%
3	5	1.4%
4	16	4.6%
5	24	6.9%
6	49	14.2%
7	37	10.7%
8	34	9.8%
9	36	10.4%
10	34	9.8%
11	26	7.5%

Value	Count	Frequency (%)
22	2	0.6%
21	1	0.3%
20	1	0.3%
18	4	1.2%
17	2	0.6%
16	8	2.3%
15	6	1.7%
14	14	4.0%
13	16	4.6%
12	28	8.1%

rain_type
Categorical

Distinct	4
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Memory size	686.0 B

no rain	341
drizzle	2
moderate rain	2
light rain	1

Length

Max length	13
Median length	7
Mean length	7.043352601
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.3%

Sample

1st row	no rain
2nd row	no rain
3rd row	no rain
4th row	no rain
5th row	no rain

Common Values

Value	Count	Frequency (%)
no rain	341	98.6%
drizzle	2	0.6%
moderate rain	2	0.6%
light rain	1	0.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
rain	344	49.9%
no	341	49.4%
drizzle	2	0.3%
moderate	2	0.3%
light	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

count_hour
Real number (ℝ_≥0)

Distinct	13
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.86416185

Minimum	12
Maximum	26
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	12
5-th percentile	12
Q1	12
median	13
Q3	15
95-th percentile	18.75
Maximum	26
Range	14
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.262879791
Coefficient of variation (CV)	0.1632179295
Kurtosis	4.932132174
Mean	13.86416185
Median Absolute Deviation (MAD)	1
Skewness	1.947849596
Sum	4797
Variance	5.120624948
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
12	112	32.4%
13	91	26.3%
14	50	14.5%
15	32	9.2%
16	19	5.5%
17	16	4.6%
18	8	2.3%
19	8	2.3%
20	5	1.4%
24	2	0.6%
Other values (3)	3	0.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
12	112	32.4%
13	91	26.3%
14	50	14.5%
15	32	9.2%
16	19	5.5%
17	16	4.6%
18	8	2.3%
19	8	2.3%
20	5	1.4%
21	1	0.3%

Value	Count	Frequency (%)
26	1	0.3%
24	2	0.6%
23	1	0.3%
21	1	0.3%
20	5	1.4%
19	8	2.3%
18	8	2.3%
17	16	4.6%
16	19	5.5%
15	32	9.2%

count_day
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	74
Distinct (%)	21.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	121.7254335

Minimum	57
Maximum	171
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.8 KiB

Quantile statistics

Minimum	57
5-th percentile	81.25
Q1	107.25
median	123
Q3	134
95-th percentile	163
Maximum	171
Range	114
Interquartile range (IQR)	26.75

Descriptive statistics

Standard deviation	23.43062014
Coefficient of variation (CV)	0.1924874651
Kurtosis	-0.288604986
Mean	121.7254335
Median Absolute Deviation (MAD)	13.5
Skewness	0.01770175112
Sum	42117
Variance	548.99396
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
130	19	5.5%
137	16	4.6%
123	11	3.2%
126	11	3.2%
115	11	3.2%
134	11	3.2%
117	10	2.9%
111	10	2.9%
124	10	2.9%
107	9	2.6%
Other values (64)	228	65.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
57	1	0.3%
70	1	0.3%
71	1	0.3%
72	1	0.3%
73	4	1.2%
74	1	0.3%
76	1	0.3%
78	1	0.3%
79	1	0.3%
80	1	0.3%

Value	Count	Frequency (%)
171	7	2.0%
168	7	2.0%
163	7	2.0%
162	3	0.9%
157	7	2.0%
155	7	2.0%
154	5	1.4%
153	6	1.7%
151	4	1.2%
148	5	1.4%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	rental_date	rental_hour	rental_day	rental_month	rental_year	holiday	dayofweek_n	dayofweek	working_day	season	peak	timesofday	temp	rhum	wdsp	rain_type	count_hour	count_day
0	12	2021-03-01	18	1	3	2021	False	0	Monday	True	Winter	True	Evening	5.3	91	8	no rain	14	107
1	29	2021-03-02	18	2	3	2021	False	1	Tuesday	True	Winter	True	Evening	4.2	88	6	no rain	14	105
2	42	2021-03-03	12	3	3	2021	False	2	Wednesday	True	Winter	False	Afternoon	5.1	87	8	no rain	14	107
3	59	2021-03-04	11	4	3	2021	False	3	Thursday	True	Winter	False	Morning	4.5	81	9	no rain	14	93
4	84	2021-03-05	16	5	3	2021	False	4	Friday	True	Winter	True	Afternoon	4.6	59	6	no rain	12	120
5	93	2021-03-06	9	6	3	2021	False	5	Saturday	False	Winter	False	Morning	4.1	64	2	no rain	13	137
6	95	2021-03-06	11	6	3	2021	False	5	Saturday	False	Winter	False	Morning	5.5	57	7	no rain	14	137
7	96	2021-03-06	12	6	3	2021	False	5	Saturday	False	Winter	False	Afternoon	6.7	57	5	no rain	13	137
8	97	2021-03-06	13	6	3	2021	False	5	Saturday	False	Winter	False	Afternoon	6.8	56	7	no rain	12	137
9	99	2021-03-06	15	6	3	2021	False	5	Saturday	False	Winter	False	Afternoon	6.5	58	9	no rain	14	137

Last rows

	df_index	rental_date	rental_hour	rental_day	rental_month	rental_year	holiday	dayofweek_n	dayofweek	working_day	season	peak	timesofday	temp	rhum	wdsp	rain_type	count_hour	count_day
336	4924	2021-11-13	14	13	11	2021	False	5	Saturday	False	Autumn	False	Afternoon	11.6	89	5	no rain	13	115
337	4961	2021-11-15	11	15	11	2021	False	0	Monday	True	Autumn	False	Morning	10.8	87	5	no rain	12	95
338	4962	2021-11-15	12	15	11	2021	False	0	Monday	True	Autumn	False	Afternoon	10.8	87	7	no rain	12	95
339	4996	2021-11-17	8	17	11	2021	False	2	Wednesday	True	Autumn	True	Morning	7.7	80	9	no rain	14	93
340	5104	2021-11-22	13	22	11	2021	False	0	Monday	True	Autumn	False	Afternoon	7.7	76	6	no rain	12	73
341	5325	2021-12-04	16	4	12	2021	False	5	Saturday	False	Autumn	False	Afternoon	3.5	85	17	no rain	12	92
342	6195	2022-01-20	18	20	1	2022	False	3	Thursday	True	Winter	True	Evening	5.1	79	4	no rain	12	81
343	6473	2022-02-03	18	3	2	2022	False	3	Thursday	True	Winter	True	Evening	11.0	73	12	no rain	12	118
344	6494	2022-02-04	17	4	2	2022	False	4	Friday	True	Winter	True	Afternoon	3.3	85	14	no rain	13	85
345	6675	2022-02-13	11	13	2	2022	False	6	Sunday	False	Winter	False	Morning	8.8	89	4	no rain	12	73

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows